vaeac_train_model_continue: Continue to Train the vaeac Model
In shapr: Prediction Explanation with Dependence-Aware Shapley Values

vaeac_train_model_continue

R Documentation

Continue to Train the vaeac Model

Description

Function that loads a previously trained vaeac model and continue the training, either on new data or on the same dataset as it was trained on before. If we are given a new dataset, then we assume that new dataset has the same distribution and one_hot_max_sizes as the original dataset.

Usage

vaeac_train_model_continue(
  explanation,
  epochs_new,
  lr_new = NULL,
  x_train = NULL,
  save_data = FALSE,
  verbose = NULL,
  seed = 1
)

Arguments

`explanation`	A `explain()` object and `vaeac` must be the used approach.
`epochs_new`	Positive integer. The number of extra epochs to conduct.
`lr_new`	Positive numeric. If we are to overwrite the old learning rate in the adam optimizer.
`x_train`	A data.table containing the training data. Categorical data must have class names `1,2,\dots,K`.
`save_data`	Logical (default is `FALSE`). If `TRUE`, then the data is stored together with the model. Useful if one are to continue to train the model later using `vaeac_train_model_continue()`.
`verbose`	String vector or NULL. Specifies the verbosity (printout detail level) through one or more of strings `"basic"`, `"progress"`, `"convergence"`, `"shapley"` and `"vS_details"`. `"basic"` (default) displays basic information about the computation which is being performed. `⁠"progress⁠` displays information about where in the calculation process the function currently is. #' `"convergence"` displays information on how close to convergence the Shapley value estimates are (only when `iterative = TRUE`) . `"shapley"` displays intermediate Shapley value estimates and standard deviations (only when `iterative = TRUE`) the final estimates. `"vS_details"` displays information about the v_S estimates. This is most relevant for `⁠approach %in% c("regression_separate", "regression_surrogate", "vaeac"⁠`). `NULL` means no printout. Note that any combination of four strings can be used. E.g. `verbose = c("basic", "vS_details")` will display basic information + details about the v(S)-estimation process.
`seed`	Positive integer (default is `1`). Seed for reproducibility. Specifies the seed before any randomness based code is being run.